tensorflow/quantum

Health v2 · maintenance only

Adoption not part of the score

2186 stars · 668 forks observed · 2026-09-30

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

TensorFlow Quantum is a Python framework for hybrid quantum-classical machine learning that integrates Cirq quantum circuits with TensorFlow/Keras. It provides high-performance quantum circuit simulation, automatic differentiation of circuits, and Keras layers for building quantum ML models.

Use cases

• build hybrid quantum-classical machine learning models
• simulate parametrized quantum circuits at scale
• train quantum circuits with TensorFlow gradients
• prototype quantum machine learning research
• compute gradients of quantum circuits with parameter shift
• run quantum circuit simulations inside Keras models

When to choose

• you want to combine Cirq quantum circuits with TensorFlow/Keras workflows
• you need fast simulation of millions of moderately-sized quantum circuits
• you are doing quantum machine learning research on NISQ-era algorithms

When to avoid

• you need to run on real quantum hardware rather than simulators
• you work outside Linux or with Python versions other than 3.10-3.12
• you need general-purpose classical deep learning without quantum components

Facets

library · maturity active

machine-learning simulation sdk quantum-computing machine-learning deep-learning python quantum-machine-learning cirq tensorflow keras hybrid-quantum-classical quantum-circuits nisq qsim linux

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For agents

markdown · JSON · MCP: product_card(name="tensorflow/quantum")